Deciding between man and zone coverage is one of the most critical strategic choices a defensive coordinator must make before each offensive play in American football. While experienced offensive coordinators and quarterbacks often rely on visual cues to identify these defensive schemes, the increasing availability of player tracking data offers a new avenue to uncover and analyze these tactics. A notable example is Amazon’s NFL Next Gen Stats model, which delivers predictions during live broadcasts (see a snapshot of the 2024 Week 12 matchup between the Pittsburgh Steelers and Cleveland Browns. These predictions, however, are often limited to analyzing specific plays without incorporating pre-snap motion which is a crucial element of modern offensive strategies.
Our project takes this model a step further. While we similarly predict man or zone coverage when the teams are set, we further leverage the additional information of pre-snap player movements. Using a hidden Markov model (HMM), we model defenders’ trajectories based on hidden states, which represent the offensive players they may be guarding. This probabilistic approach allows us to infer dynamic coverage patterns during plays with pre-snap motion. To exploit this high-dimensional time-series data, we calculate the entropy of the state probabilities, thus, producing measures of uncertainty for each play. Incorporating these as covariates into the existing pre-motion model significantly improves both the AUC and detection accuracy.
In doing so, we present a data-driven framework that evaluates the effectiveness of pre-snap motion in uncovering defensive strategies, providing real-time tactical insights for coaches.
We analyze tracking data from nine weeks of the NFL 2022 season, provided by the NFL Big Data Bowl 2024. Beside the tracking data, we also use information on plays and players. We further considered the corresponding data from PFF that assigned the categories , and representing the different schemes to each play. As it is not properly described what means, we omit every play that is associated with this value. Moreover, we omit plays with more than five offensive linemen and with two quarterbacks and those plays that did not contain any pre-snap motion. Then, we end up with XY offensive plays in total, from which the defense played Y in zone and X in man coverage.
Within these plays, we concentrate on the tracking data after the line has been set (because we are not interested in how players come out of the huddle) and before the ball has been snapped by the Center.
To accurately forecast the defensive scheme (man- or zone defense) for every play, we need to create various features derived from the tracking data. In particular, we conducted the following feature engineering steps: ROBERT
Our analysis comprises different steps:
We train different model (LASSO, Random Forest, XGBoost) to predict whether the defense plays a man- or zone coverage scheme. In particular, …..
The model uses the previously described features, blablabla.
ROBERT
We model the movements of defensive players during the phase of pre-snap motion within a hidden Markov framework, in which the underlying states represent the offensive players to be guarded (see Franks et al. 2015 for a similar approach in basketball).
OLE
The following video displays a touchdown from the Kansas City Chiefs against the Arizona Cardinals in Week 1 of the 2022 NFL season. We can see that, pre-snap, Mecole Hardman (KC #17) is in motion. He is immediately followed by the defender Marco Wilson (AZ #20), which is a clear indication for man-coverage.